Disease Risk Analysis Using Genetic and Temporal Data Stratification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current disease risk analysis methods using polygenic risk scores (PRS) struggle to accurately predict disease onset and complication development over time, as they fail to account for individual genetic differences and temporal health data variability, leading to inadequate preventive measures.
Innovation Solution
A disease risk analysis apparatus that acquires genetic and temporal health data, determines thresholds for stratification, generates stratified data, and analyzes temporal data based on set criteria to identify specific observation targets, allowing for detailed analysis of disease risk and complication development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple PRS stratification is used to analyze temporal data, then the analysis process is simple and easy to implement, but the prediction accuracy of disease risk and complication development is insufficient
Solution Approach 1:
The patent segments the analysis process into multiple stages: first stratifying temporal data by PRS to identify high-risk individuals, then further stratifying by disease onset timing (early vs. late onset), and finally analyzing complication development within each subgroup. This multi-level segmentation allows for more precise prediction while maintaining systematic organization of the complex analysis process
Solution Approach 2:
The patent adds temporal dimension to the traditional PRS analysis by incorporating time-stratified data. Instead of analyzing cross-sectional data only, the method analyzes longitudinal health checkup data across multiple time points, adding the time dimension to enable prediction of disease onset timing and complication development trajectories
2Measurement precision
If temporal data is collected and analyzed in detail to improve prediction accuracy, then the precision of disease risk prediction is improved, but the complexity of data collection and analysis increases
Solution Approach 1:
The patent utilizes existing health checkup data that can serve multiple purposes: the same longitudinal data is used for both PRS stratification and disease onset timing analysis, and further for complication development analysis. This multi-functional use of the same data collection system reduces the need for separate specialized data collection mechanisms
Solution Approach 2:
The patent performs PRS stratification and identification of high-risk individuals in advance, before detailed temporal analysis is conducted. This preliminary action allows for targeted analysis of temporal data only for those who need it, reducing the overall computational complexity and data processing requirements
3Device complexity
If PRS stratification is used without considering temporal data, then the analysis method is simple, but it cannot capture individual differences in disease progression speed and complication development
Solution Approach 1:
The patent transitions from static cross-sectional PRS analysis to dynamic longitudinal analysis by incorporating time-varying health data. The method tracks changes in health indicators over time within each PRS stratum, enabling detection of individual differences in disease progression speed and complication development trajectories that static analysis would miss
Data Source
AI summary
According to one embodiment, a disease risk analysis apparatus includes a processor. The processor acquires healthcare data including genetic score data and temporal data collected over time. The processor determines a threshold for stratifying a genetic score. The processor stratifies the genetic score data based on the threshold. The processor sets a criterion for at least a test value of the temporal data. The processor generates first observation target data from the temporal data. The processor generates starting point data based on the first observation target data and the criterion. The processor determines an observation target from the temporal data. The processor generates second observation target data.